While chemical reactions involve complex underlying dynamics such as quantum mechanics they can generally be modeled quite well at a higher level using factors such as reactant and product concentration and reaction-rate constants. However, the realistic simulation of chemical reactions and its expansion to the analysis of chemical reaction networks, should include stochastic simulation, because, for example, it is impossible to predict exact molecular population levels through time without considering the kinetic properties of all involved chemical species.
Recent advancements in proteomics and genomics have shed significant light on the individual reactions making up the complex biochemical reaction networks operating in living organisms and these finding would be well complimented by comprehensive and accurate methods for simulating such biochemical networks. However, it is especially important to model stochastics when simulating the biochemical networks, as cells process their mechanical and chemical inputs with highly noisy and imprecise processes that, very often, involve relatively few molecules. Such molecular-dynamics simulations are extremely computationally intensive, especially when the effects of noise, nonlinearity, network-feedback effects, and cell-to-cell variability are included. In spite of efficient stochastic algorithms being available, the computation time of these molecular dynamics simulations increases precipitously when stochastics are included. For example, the most computationally-expensive part of the Gillespie algorithm for simulating chemical reactions is the generation of exponentially-distributed random numbers, which consumes approximately 98% of process time. As a result, the real-time simulation of just 30 state variables with stochastics is quite challenging to implement. The simulation of large-scale reaction networks in cells, which each have up to 30,000 state-variables, is simply beyond the practical limit of traditional computer-based simulation techniques.
One method for reducing the computational expense of simulating reaction networks includes augmenting the digital simulation with a custom analog integrated circuit for generating exponentially-distributed random numbers. This approach is reported to provide a potential speed-up of approximately two orders of magnitude over purely software implementations of the Gillespie algorithm. However, this speed-up is still inadequate for the practical simulation of large-scale reaction networks.
It therefore be desirable the have a system and method for simulating large-scale chemical and biochemical networks that accurately includes stochastics and provides a significant increase in simulation speed over traditional techniques